#############################################
###### Replication Code for Figure A11 ######
########### January 9th, 2025 ###############
#############################################

rm(list=ls())

### Script produces Figure A11 and saves it in the `/figures` folder

############ Balancing on predictors ################

library(dplyr)
library(fixest)
library(ggplot2)
library(stringr)
library(hbal)
library(gt)
library(modelsummary)


#### Figure 11

df <- readRDS("data/workfile.rds")


w <- readRDS("data/weighted_party_feeling.rds")

df$parfam_nat <- ifelse(df$parfam_name == "Nationalist parties", 1, 0)
df$parfam_left <- ifelse(df$parfam_name == "Socialist/left parties", 1, 0)
df$parfam_con <- ifelse(df$parfam_name == "Conservative parties", 1, 0)

df$out_feeling <- scale(df$mean_outparty_feeling) + scale(df$mean_close_num)

df <- df %>% left_join(w)

control_variables <- c('high_pol_interest',
                       "high_edu", "medium_edu", "low_edu",  "dem_age",
                       "parfam_nat", "mean_close_num", "mean_outparty_feeling",
                       "coDE", "coFR", "coES", "coNL", "coUS", "coPL", 
                       "coGR", "coIT", "coSE", "d_affiliated", 
                       "high_income", "medium_income", "low_income", "in_party_feeling",
                       "lrscale")

df$trust_in_opposing_parties = scale(df$trust_in_opposing_parties)

nomiss_df <- df %>% 
  select(open_immig, dem_female, dem_country_code, dem_education_level,
         dem_country_code, dem_rural, dem_age, lrscale, parfam_name,
         high_income, medium_income, low_income, political_interest_num,
         high_edu, medium_edu, low_edu, treated, mean_outparty_feeling, 
         high_pol_interest, mean_close_num, d_affiliated, treated,
         political_interest, treated_econ, treated_culture, out_feeling,
         trust_in_opposing_parties, in_party_feeling) %>% 
  mutate(coDE = ifelse(dem_country_code == "DE", 1, 0), 
         coFR = ifelse(dem_country_code == "FR", 1, 0), 
         coNL = ifelse(dem_country_code == "NL", 1, 0),
         coUS = ifelse(dem_country_code == "US", 1, 0),
         coPL = ifelse(dem_country_code == "PL", 1, 0),
         coGR = ifelse(dem_country_code == "GR", 1, 0), 
         coIT = ifelse(dem_country_code == "IT", 1, 0), 
         coSE = ifelse(dem_country_code == "SE", 1, 0), 
         coGB = ifelse(dem_country_code == "GB", 1, 0), 
         coES = ifelse(dem_country_code == "ES", 1, 0), 
         parfam_liberal = ifelse(parfam_name == "Liberal parties", 1, 0), 
         parfam_other = ifelse(parfam_name == "Other parties", 1, 0), 
         parfam_eco = ifelse(parfam_name == "Ecological parties", 1, 0), 
         parfam_nat = ifelse(parfam_name == "Nationalist parties", 1, 0), 
         parfam_left = ifelse(parfam_name == "Socialist/left parties", 1, 0), 
         parfam_cd = ifelse(parfam_name == "Christian democratic parties", 1, 0), 
         parfam_nonvoters = ifelse(parfam_name == "Non-voters", 1, 0), 
         parfam_sd = ifelse(parfam_name == "Social democratic parties", 1, 0), 
         parfam_con = ifelse(parfam_name == "Conservative parties", 1, 0), 
         parfam_ethnic = ifelse(parfam_name == "Ethnic and regional parties", 1, 0),
         parfam_spec = ifelse(parfam_name == "Special issue party", 1, 0),) %>% 
  .[complete.cases(.), ]

coef_df <- data.frame()

########## Culture - No immigration
nomiss <- nomiss_df %>% 
  filter((open_immig == 0 & treated_culture == 1) | treated == 0) 

hbal.out <- hbal(Treat = 'treated', 
                 X = control_variables,  
                 Y = 'trust_in_opposing_parties',  data = nomiss)
m1 = att(hbal.out, dr = FALSE)
no_discussion_culture <- m1


temp <- data.frame(coef = c(m1$Estimate[1]),
                   se = c(m1$`Std. Error`[1]), 
                   name = "Did not discuss immigration",
                   var = "Trust", 
                   group = "Cultural issues \n condition")


coef_df <- rbind.data.frame(coef_df, temp)



########## Culture - Immigration
nomiss <- nomiss_df %>% 
  filter((open_immig == 1 & treated_culture == 1) | treated == 0) 

hbal.out <- hbal(Treat = 'open_immig', 
                 X = control_variables,  
                 Y = 'trust_in_opposing_parties',  data = nomiss)
m1 = att(hbal.out, dr = FALSE)
discussion_culture <- m1

temp <- data.frame(coef = c(m1$Estimate[1]),
                   se = c(m1$`Std. Error`[1]), 
                   name = "Discussed immigration",
                   var = "Trust", 
                   group = "Cultural issues \n condition")

coef_df <- rbind.data.frame(coef_df, temp)



########## Economics - Immigration
nomiss <- nomiss_df %>% 
  filter((open_immig == 1 & treated_econ == 1) | treated == 0) 

hbal.out <- hbal(Treat = 'open_immig', 
                 X = control_variables,  
                 Y = 'trust_in_opposing_parties',  data = nomiss)
m1 = att(hbal.out, dr = FALSE)
discussion_econ <- m1

temp <- data.frame(coef = c(m1$Estimate[1]),
                   se = c(m1$`Std. Error`[1]), 
                   name = "Discussed immigration",
                   var = "Trust", 
                   group = "Economic issues \n condition")

coef_df <- rbind.data.frame(coef_df, temp)


########## Economics - No immigration
nomiss <- nomiss_df %>% 
  filter((open_immig == 0 & treated_econ == 1) | treated == 0) 

hbal.out <- hbal(Treat = 'treated', 
                 X = control_variables,   
                 Y = 'trust_in_opposing_parties',  data = nomiss)
m1 = att(hbal.out, dr = FALSE)
no_discussion_econ <- m1

temp <- data.frame(coef = c(m1$Estimate[1]),
                   se = c(m1$`Std. Error`[1]), 
                   name = "Did not discuss immigration",
                   var = "Trust", 
                   group = "Economic issues \n condition")

coef_df <- rbind.data.frame(coef_df, temp)



pd <- position_dodge(0.5)

coef_df <- coef_df %>% filter(var == "Trust")
coef_df1 = coef_df

regs <- ggplot(coef_df, aes(x = group,  y = coef,  color = name)) + 
  geom_point(position = pd, shape = 21, fill = 'white') + 
  geom_errorbar(aes( ymin = coef - se*1.959964, ymax = coef + se*1.959964), width = 0, position = pd) + 
  geom_errorbar(aes( ymin = coef - se*1.644854, ymax = coef + se*1.644854), width = 0, position = pd, linewidth = 1.2) + 
  geom_hline(yintercept = 0, color = "red", linetype = "dashed") +
  scale_colour_manual(values = c("grey", "black")) + 
  xlab("") + 
  ylab("Effect of discussing immigration on out-partisan trust") +
  coord_flip() +
  theme_bw() + 
  guides(color=guide_legend(title=""))

regs
ggsave("figures/FigA11_entropy_balancing_in_party_feeling.pdf",
       height = 4, width = 6)



